The cover illustrates that the analysis, via machine learning, of near-infrared-fluorescence emissions of carbon-nanotube sensors placed in serum samples can be used to predict ovarian cancer.
Spectral fingerprinting of ovarian cancer in serum samples
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Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning - Nature Biomedical Engineering
Ovarian cancer can be predicted with high sensitivity and specificity via a fingerprint obtained, via machine learning, from near-infrared fluorescence emissions of an array of carbon nanotube sensors in serum samples.